[AI Seminar] Fwd: Forwarding ICML AI + Climate Change Workshop call for submissions?

[Han Zhao]

FYI. ICML AI + Climate Change Workshop CFP: https://www.climatechange.ai/

---------- Forwarded message ---------
发件人： Priya Donti <pdonti at andrew.cmu.edu>
Date: 2019年4月2日周二 下午2:00
Subject: Forwarding ICML AI + Climate Change Workshop call for submissions?
To: Han Zhao <han.zhao at cs.cmu.edu>, Zico Kolter (CMU) <zkolter at cs.cmu.edu>


Hi Han and Zico,

Would you be willing to forward this call for submissions on to the AI
seminar mailing list?

Thanks!
Priya

---------- Forwarded message ---------
From: Priya Donti <pdonti at andrew.cmu.edu>
Subject: ICML AI + Climate Change Workshop - call for submissions

*** CALL FOR SUBMISSIONS: ICML workshop “Climate Change: How Can AI Help?”
***

We invite submission of extended abstracts applying machine learning to the
problems of climate change. There will be three tracks (Deployed, Research,
and Ideas).

Date:  June 14 or 15, 2019

Location:   Long Beach, California, USA

Website: www.climatechange.ai

Submission deadline:  April 30, 11:59 PM Pacific Time

Notification: May 15 (early notification possible upon request)

Submission website:  https://cmt3.research.microsoft.com/CCAI2019
Contact: climatechangeai.icml2019 at gmail.com

Summary

-------------

Climate change is widely agreed to be one of the greatest challenges facing
humanity. We already observe increased incidence and severity of storms,
droughts, fires, and flooding, as well as significant changes to global
ecosystems, including the natural resources and agriculture on which
humanity depends. The 2018 UN report on climate change estimates that the
world has only thirty years to eliminate greenhouse emissions completely if
we are to avoid catastrophic consequences.

Many in the machine learning community want to address climate change but
feel their skills are inapplicable. This workshop will showcase the many
settings in which machine learning can be applied to reducing greenhouse
emissions and helping society adapt to the effects of climate change.
Climate change is a complex problem requiring simultaneous action from many
directions. While machine learning is not a silver bullet, there is
significant potential impact for research and implementation.

About ICML

----------------

ICML is one of the premier conferences on machine learning, and includes a
wide audience of researchers and practitioners in academia and industry. It
is possible to attend the workshop without either presenting or attending
the main ICML conference. Those interested should register for the
Workshops component of ICML at https://icml.cc/ while tickets last (a
number of spots will be reserved for accepted submissions).

Call for submissions

---------------------------

We invite submission of extended abstracts on machine learning applied to
problems in climate mitigation, adaptation, or modeling, including but not
limited to the following topics:

- Power generation and grids

- Transportation

- Smart buildings and cities

- Industrial optimization

- Carbon capture and sequestration

- Agriculture, forestry and other land use

- Climate modeling

- Extreme weather events

- Disaster management and relief

- Societal adaptation

- Ecosystems and natural resources

- Data presentation and management

- Climate finance

Accepted submissions will be invited to give poster presentations at the
workshop, of which some will be selected for spotlight talks.  Please
contact climatechangeai.icml2019 at gmail.com with questions, or if visa
considerations make earlier notification important.

Dual-submissions are allowed, and the workshop does not record proceedings.
Submissions will be reviewed double-blind; do your best to anonymize your
submission, and do not include identifying information for authors in the
PDF. We encourage, but do not require, use of the ICML style template
(please do not use the “Accepted” format).

Submission tracks

------------------------

Extended abstracts are limited to 3 pages for the Deployed and Research
tracks, and 2 pages for the Ideas track, in PDF format. An additional page
may be used for references. All machine learning techniques are welcome,
from kernel methods to deep learning. Each submission should make clear why
the application has (or could have) positive impacts regarding climate
change. There are three tracks for submissions:

DEPLOYED

* Work that is already having an impact *

Submissions for the Deployed track are intended for machine learning
approaches which are impacting climate-relevant problems through consumers
or partner institutions. This could include implementations of academic
research that have moved beyond the testing phase, as well as results from
startups/industry. Details of methodology need not be revealed if they are
proprietary, though transparency is encouraged.

RESEARCH

* Work that will have an impact when deployed *

Submissions for the Research track are intended for machine learning
research applied to climate-relevant problems. Submissions should provide
experimental or theoretical validation of the method proposed, as well as
specifying what gap the method fills. Algorithms need not be novel from a
machine learning perspective if they are applied in a novel setting.

Datasets may be submitted to this track that are designed to permit machine
learning research (e.g. formatted with clear benchmarks for evaluation). In
this case, baseline experimental results on the dataset are preferred but
not required.

IDEAS

* Future work that could have an impact *

Submissions for the Ideas track are intended for proposed applications of
machine learning to solve climate-relevant problems. While the least
constrained, this track will be subject to a very high standard of review.
No results need be demonstrated, but ideas should be justified as
extensively as possible, including motivation for the problem being solved,
an explanation of why current tools are inadequate, and details of how
tools from machine learning are proposed to fill the gap.

Organizers

---------------

David Rolnick (UPenn)

Alexandre Lacoste (ElementAI)

Tegan Maharaj (MILA)

Jennifer Chayes (Microsoft)

Yoshua Bengio (MILA)

Karthik Mukkavilli (MILA)

Di Wu (MILA)

Narmada Balasooriya (ConscientAI)

Priya Donti (CMU)

Lynn Kaack (CMU)

Manvitha Ponnapati (MIT)



-- 

*Han ZhaoMachine Learning Department*


*School of Computer ScienceCarnegie Mellon UniversityMobile: +1-*
*412-652-4404*
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